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Found 4 papers, 4 papers with code
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks.
Federated Learning of Generative Image Priors for MRI Reconstruction
Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents.
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
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